Ambulatory blood pressure monitoring (ABPM) has been widely used over casual office blood pressure (BP) readings to improve the diagnosis and treatment of hypertension and to assess cardiovascular risk [1, 2]. ABPM is a fully automated technique in which BP measurements are taken at regular intervals (usually every 15 to 30 minutes) over a 24 or 48 hour period, providing a continuous BP record during the patient's normal daily activities. The use of ABPM has allowed for the observation of a circadian BP pattern. Typically, there is a decrease in systolic and diastolic BP levels during periods of sleep. Subjects who exhibit a nocturnal BP drop of at least 10% are classified as dippers, the ones who do not show this drop are called non-dippers. Recent studies have shown that non-dipper BP patterns are associated with an increased frequency of cardiovascular events, as well as target-organ damage, and cardiovascular morbidity and mortality [3, 4, 5, 6, 7, 8].
The correct assessment of dipper vs. non-dipper requires the ability to accurately identify activity and rest cycles. Traditionally, determination of activity and rest cycles has been performed by assuming or imposing a fixed schedule (for instance, the activity cycle spanning from 7:00 h to 23:00 h, and the rest cycle from 23:00 h to 7:00 h) [9]. This method can prove highly inaccurate due to individual differences in sleep habits. Another method involves the use of a diary where the subjects under study keep a record of their going to sleep and wake up times. In practice, diaries have proven to be cumbersome and unreliable. Several studies have explored the possibility of using actigraphy to perform identification of sleep/wake cycles, which provides an inexpensive and non-obtrusive method to discriminate between sleep and wake periods based on recorded activity levels [10, 11, 12, 13]. The typical actigraphs are wrist-worn devices that use accelerometers to measure and record movement counts at uniform time intervals with low sampling frequencies (e.g. 1 sample per minute). The actigraphy signal can be used to discriminate between sleep and wake cycles, but an automatic method is needed to perform the identification of activity and rest cycles objectively and accurately. In the last 20 years, several methods have been proposed to automatically identify sleep and wake periods from actigraphy [14, 15, 16]. More recent methods use additional signals such as the electrocardiogram and respiration to achieve better performance [17]. Most of these methods have been designed as an alternative to polysomnography for the study of sleep/wake patterns in patients with sleep disorders and other conditions affecting quantity and quality of sleep [18, 19, 20, 21, 22, 23, 24, 25]. As a consequence, they exhibit a high sensitivity to sleep disturbances, and tend to generate multiple wake identifications during main rest periods. This makes them unsuitable for the identification of activity and rest cycles in the context of ABPM and cardiovascular risk assessment, where the objective is not to detect wake events during periods of sleep, but rather to determine the boundaries between the main activity and rest periods. For this reason, short transitions between states during a main activity or rest cycle are considered invalid in this application.
Currently, there are no methods available to perform automatic activity/rest identification from actigraphy with the accuracy required in clinical applications involving cardiovascular risk assessment using ABPM.